Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

MOTS-annotated UAV Vineyard Dataset captured using Multiple Perspectives to avoid Leaf Occlusion for Object Detection and Tracking

This dataset contains UAV RGB videos (MP4) recorded with a Phantom4 RTK in a vineyard during the harvesting campaign of 2023. It also includes frames and annotations (PNG) to boost Object Detection and Tracking of grape bunches. There are two types of videos: (1) videos capturing the side of the canopy from a frontal point of view only, and (2) videos that collect the data from multiple perspectives to avoid leaf occlusion, common in commercial vineyards. All flights were executed 3 meters above ground level, with a clear sky and wind speed below 0.5 m/s.

opencc-by-4.0Dec 2023View details →

Objective sleep quality predicts subjective sleep ratings: a multiday observational study

This dataset serves as supplementary material for the referenced article.

opencc-by-4.0Feb 2024View details →

Dataset of wood identifications of Congolese heritage objects – constituting 7% of the RMCA's collection (Royal Museum for Central Africa, Belgium)

The Belgian Royal Museum for Central Africa (RMCA) hosts a large body of wooden objects from the Democratic Republic of the Congo: more than 55.000 sculptures, musical instruments, equipment, furniture, and alike are made of wood or contain wooden elements. For 3814 objects – roughly 7% of the museum’s wooden heritage objects- the wood species has been identified. The majority of these identifications, which were never published before, were performed by Roger Dechamps,  principal curator of the Tervuren xylarium from 1965 to 1995. Identification was needed in the context of upcoming exhibitions or loans, and while the selection of objects was varied, it is representative of the museum's broader collection of heritage objects. Dechamps studied the Congolese objects by means of invasive techniques. To observe the transverse plane of the wood he cleared a small area on the object with a scalpel, most often, on the bottom of the object. He studied the anatomical features with a loupe. In addition to the visual inspection of the transverse plane, he sampled the objects along the tangential plane. Cutting a slice from the objects with a scalpel, the sample was then imbedded and viewed with optical microscopy. A large part of these tangential thin sections are still preserved within the Tervuren xylarium. The dataset is in pdf form (78 pages), and contains 3961 entries. 58 objects were sampled and analysed multiple times (because the object contains multiple types of wood species). These entries are indicated in grey. 184 objects were sampled and analysed, but could not be identified.  The information in the excel file is ordered as follows: Column 1: The object's identifier following the RMCA's registration system (https://www.africamuseum.be/en/research/collections_libraries/human_sciences/collections/database) Column 2: The taxonomic identification by Roger Dechamps Column 3: Object typology  Column 4 Culture or region of origin in the Democratic Republic of the Congo  Column 5: Date of wood species identification Maes, F. and Beeckman, H. 1999. Wood to Survive. Liber Amoricum for Roger Dechamps. Annalen Economische Wetenschappen KMMA (25).

opencc-by-4.0Feb 2024View details →

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects

<h1>Datasets and analysis code of the following publication:</h1> Peng Liu, Ke Bo, Mingzhou Ding and Ruogu Fang (2024). Emergence of Emotion Selectivity in Deep Neural Networks  Trained to Recognize Visual Objects. PLOS Computational Biology. DOI: 10.1371/journal.pcbi.1011943 For any questions please contact the first author at mail pliu1 [at] ufl [dot] edu <h2>Contents:</h2>  Code_DataAnalysis   - Extracted Selectivity         -- IAPS and NAPS datasets         -- Neurons In Alexnet and VGG networks         --Networks are pre-trained on ImageNet and randomly initialized   - Extracted Overlapped Selectivity across IAPS and NAPS.   - Extracted tuning performance changes from two datasets and the VGG network   -Code to replicate the key results including         --Tuning quality        -- Number of overlapped neurons       -- Enhance neuron activity      -- Lesion neurons  TrainedNetworks        --Pre-trained VGG network on ImageNet         --Pre-trained Alexnet network on ImageNet After pre-training these networks on ImageNet, we fixed their weights and trained them to classify pleasant, neutral, and unpleasant images into three emotion categories using both IAPS and NAPS datasets.   Image datasets Access image datasets by request from https://csea.phhp.ufl.edu/media/iapsmessage.html for IAPS and https://lobi.nencki.edu.pl/research/8/ for NAPS.

Temporal Event Knowledge Graphs transformed from Object-Centric Event Logs

In the paper"Transforming Object-Centric Event Logs to Temporal Event Knowledge Graphs", we introduced and formalized temporal Event Knowledge Graphs (tEKGs) and presented an algorithm to transform Object-Centric Event Logs (OCEL) 2.0 into tEKGs. Data sets are the results of transforming OCEL 2.0 log files. The source of the generated dump files are as follows: <ol> <li>ContainerLogistics.neo4j.dum : <a href="../records/8428084">Link to the source data</a></li> <li>OrderManagement.neo4j.dump: <a href="../records/8428112">Link to the source data</a></li> <li>Procure-To-Payment.neo4j.dump: <a href="../records/8412920">Link to the source data</a></li> </ol> The version of the dump files is 5.12.0. Additionally, for restoring dump files inside Neo4j, you need to enter the username and password, which is indicated below: Username: neo4j Password: 12345678

restrictedcc-by-4.0Mar 2024View details →

Data for project "Cellulose nitrate lacquer on silver objects", RAÄ-2021-3131

Data from the project "Cellulose nitrate lacquer on silver objects" (RAÄ-2021-3131). Each zip file contains an Instrument report file which gives sample names and descriptions, instrument parameters, spectra figures and in some report interpretation notes. Data from the following analytical techniques is included: 1. Scanning electron microscopy with energy dispersive x-ray spectroscopy (SEM-EDS); 2. Polarized light microscopy (PLM); 3. Fourier transform infrared spectroscopy (FTIR). Data was collected as a continuation of (but after the publication of) the bachelor theses "A greener solution: Investigating the potential use of Green Solvents to remove cellulose nitrate lacquer from silver objects" by Evelina Borén (https://hdl.handle.net/2077/72648) and "The use of gels for the removal of cellulose nitrate lacquer on silver" by Katayon Miri (https://hdl.handle.net/2077/72650), both at the Department for Conservation, University of Gothenburg. The project is also part of a larger invesitgation about removing cellulose nitrate lacquer on silver objects, supported by European Union funded IPERION HS project (2020-INFRAIA-2019-1),....... Instrument reports including technical specifications of each instrument can also be found under DOI 10.5281/zenodo.10869595 .

opencc-by-4.0Jul 2021View details →

Multi-objective particle swarm optimization and training of datasheet-based load dependent lithium-ion voltage models

Open the record for dataset details and reuse information.

TOPSPIN: daTaset Of sPinning objectS with neuromorPhic vIsioN

Dataset used in the paper "<a href="https://arxiv.org/abs/2405.06216">Event-based Structure-from-Orbit</a>", for the purpose of sparse point cloud reconstruction of a spinning object. The dataset consists of six objects rotating at three speeds, being viewed by a static event camera from four different perspectives. For each scene, one of the objects was placed on the turntable and rotated at a given speed, while the camera observed it from a given perspective. We exhaustively enumerated all combinations of objects, rotational speeds and perspectives to generate 72 scenes in total.   Scene names use the following encoding: {object}-{perspective}-{speed} All scenes have the same data format: scene/     events.csv -- Event stream with the format {timestamp, x, y, polarity (0=off, 1=on)}     calib.txt -- Camera calibration file, with the format "fx fy cx cy k1 k2 p1 p2 k3"     meta.json -- Metadata file with camera frame dimensions Note: all timestamps are in microseconds.   When using the data in an academic context, please cite the following paper. <pre><code>@inproceedings{elms2024eventbased,       title={Event-based Structure-from-Orbit},     booktitle={IEEE Conf. on Computer Vision and Pattern Recpgnition},       author={Ethan Elms and Yasir Latif and Tae Ha Park and Tat-Jun Chin},       year={2024}, }</code></pre>

A fine-grained dataset named iSOOD for sewage outfalls objective detection in natural environments

Basic Information: The 10481 images in iSOOD were captured using UAVs and handheld cameras by individuals from the river basin in China. Our study has carefully annotated these images to ensure accuracy and consistency. The iSOOD has undergone technical validation utilizing the YOLOv5 objective detection model. The iSOOD have been publicly released after undergoing desensitization, with the goal of promoting interdisciplinary collaboration and accelerating advancements in the intelligence watershed management. We expect that the iSOOD dataset is anticipated to inspire further research on the SOs detection and the control of pollution migration paths, and serve as a fundamental resource for the use of advanced deep learning visual technology in environmental monitoring. Usage Policy: If you plan to use our data in a scientific analysis paper, we strongly recommend contacting us in advance to seek opinions, and consider our contributions in the acknowledgments or as co-authors.

Implementation and Results of a Randomized Local Search on the 2D Rectangular Bin Packing Problem with Item Rotation using Different Objective Functions

1. Introduction In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) applied to the two-dimensional bin packing problem without orientation (where items can be rotated by 90 degrees). As benchmark dataset, we use the <code>beng</code>, <code>A</code>, and <code>class</code> instances from <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> as well as the four non-trivial Almost Squares in Almost Squares (<a href="https://math.vu.nl/~sbhulai/publications/data_analytics2016b.pdf"><code>Asqas</code></a>) instances These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied. Rui Zhao, Tianyu Liang, Zhize Wu, Daan van den Berg, Matthias Thürer, and Thomas Weise. 2024. Randomized Local Search on the 2D Rectangular Bin Packing Problem with Item Rotation. In Genetic and Evolutionary Computation Conference (GECCO'24 Companion), July 14–18, 2024, Melbourne, VIC, Australia. ACM, New York, NY, USA, 4 pages. doi:<a href="https://doi.org/10.1145/3638530.3654139">10.1145/3638530.3654139</a>. To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> version 0.8.34 and <a href="https://thomasweise.github.io/moptipy">moptipy</a> version 0.9.98, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case. 2. Directory Structure This archive contains the following directories: <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>data</code> is the directory with the results and their evaluation.</li> <li><code>data/results</code> is the directory with the log files generated by the experiment. In this folder, there are two sub-folders, <code>ibf1</code> and <code>ibf2</code>. We tested two different encodings, but found that the second one (<code>ibf2</code>) is too slow to do meaningful experiments. Thus, the experiments with it were abandoned and only one objective function was tested. We include <code>ibf2</code> for the sake of completeness, whereas <code>ibf1</code> was used in our paper. Either way, both <code>ibfX</code> folders contain one directory for each objective function applied to them. In each such directory, there is one folder (for the single algorithm applied) and this folder, in return, contains one folder per benchmark instance. The benchmark instance folders contain the three log files of the three runs that we applied to each instance/algorithm/objective combination. Each log file contains information of one run, i.e., one execution of one algorithm on one problem instance. All improving moves of a run as well as the final solution are stored in the log file.</li> <li><code>data/evaluator</code> is the folder containing Python scripts that were used to evaluate these results. Two scripts are provided: <code>evaluator_short.py</code> was used for generating the tables used in the final paper version. <code>evaluator_full.py</code> provides larger tables, which could not be included in the final paper due to space reasons.</li> <li>Folder <code>evaluation_full</code> was generated using <code>evaluator_full.py</code> and contains tables and figures and a result summary in CSV format.</li> <li>Folder <code>evaluation_short</code> was generated using <code>evaluator_short.py</code> and contains both the tables used in the paper as well as a result summary in CSV format.</li> </ul> 3. License The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> and other benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive. 4. Contact If you have any questions or suggestions, please contact Mr. Rui ZHAO (赵睿) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:zr1329142665@163.com">zr1329142665@163.com</a>.

Dataset and mesh convergence study for the article: Implementing moving object capability in a two-phase Eulerian model for sediment transport applications

This dataset includes the results for the article  Implementing moving object capability in a two-phase Eulerian model for sediment transport applications (2024). OpenFOAM Journal. The dataset consists of the results obtained to produce the figures of the manuscript and the mesh convergence test.

Bullying and objective sleep

The present study investigates the short-term effects of workplace bullying on objective sleep patterns using an actigraphy diary approach.

The relationship between contextual factors and code smells: contextual factors, code smell tables and the investigated object software

This dataset has research data regarding the impact of contextual factors on the incidence of code smells and is organized as follows:   - In the 'datasetcodesmellsandcontextualfactors' folder there is the collected data referring to the contextual factors and code smells of the 419 systems used as a sample in the research   - In the 'datasetonlycontextualfactors' folder there is data on the contextual factors of more than 450,000 software hosted on Github   - In the 'softwares' folder, the 419 software that were used in the study are available, to enable further studies on this dataset In each folder there is a guide explaining the available fields and the structure of the folder

Seurat_object_merged_PBMCs

This dataset includes a Seurat object consisting of the merged count data from PBMCs of patients included in the study "Genomic Insights into Pediatric Intestinal Inflammatory and Eosinophilic Disorders using Single-sell RNA-sequencing." This is the unfiltered, unmapped data after reading the CellRanger count data into Seurat. It contains singlets identified via demuxlet. Barcodes that were not mapped to a genotype or were found not to be singlets were removed.

opencc-by-4.0Nov 2024View details →

EBAII n1 scRNAseq : Training intermediate Seurat (v5) objects

Intermediate Seurat (v5) objects produced for the EBAII n1 single cell RNAseq training. <ul> <li><code>01_TD3A_S5_Metrics.Tech_31053.4587.RDS</code> : Technical metrics (nFeature, nCount, ...)</li> <li><code>02_TD3A_S5_Metrics.All_31053.4587.RDS</code> : Biological metrics (%mito, %ribo, %stress)</li> <li><code>03_TD3A_S5_CC_31053.4587.RDS</code> : Estimated cell cycles scores and phase</li> <li><code>04_TD3A_S5_Metrics.Filtered_12508.4278.RDS</code> : Cells and features filtered according to metrics</li> <li><code>05_TD3A_S5_Doublets.filtered_12508.4035.RDS</code> : Cell doublets removed</li> <li><code>06_TD3A_S5_LogNorm_12508.4035.RDS</code> : LogNormalization applied</li> <li><code>07_TD3A_S5_Scaled.2k_12508.4035.RDS</code> : Scaling using 2000 HVGs</li> <li><code>08_TD3A_S5_Scaled.2k_Reg.PCrb_12508.4035.RDS</code> : Same with regression for <code>percent_rb</code> (% ribo)</li> <li><code>09_TD3A_S5_DimRed.PCA_12508.4035.RDS</code> : PCA with 50 components</li> <li><code>10_TD3A_S5_Clustered.0.8_12508.4035.RDS</code> : Louvain clustering with resolution 0.8</li> <li><code>11_TD3A.TDCT_S5_Merged_12926.3886</code> : Merging of the pre-processed TD3A and TDCT samples (each halved for its cells, using random subsampling with the seed 1337)</li> <li><code>12_TD3A.TDCT_S5_Integrated_12926.3886.RDS</code> : Results of TD3A and TDCT samples integration using 4 different methods (Seurat's CCA, RPCA, Harmony ; and standalone Harmony), and Louvain clustering for each (res 0.8)</li> <li><code>13_TD3A.TDCT_S5_Integrated_Annotated.RDS</code> : Results of the automatic cell type annotation</li> </ul>

Transsaccadic perception of changes in object regularity

Dataset from the following publication: Sharvashidze, N., Valsecchi, M., & Schütz, A. C. (2024). Transsaccadic perception of changes in object regularity. Journal of Vision, 0(0):09163, 1–21, https://doi.org/10.1167/jov.0.0.09163.

opencc-by-4.0Aug 2024View details →

Objective African Easterly Tracks in Reanalysis Data using "QTrack"

Objective track data for African Easterly Waves in reanalysis data (ERA5 and MERRA2) through 2024. Waves are tracked from June 1st through October 31st of each year. These tracks are produced using <a href="https://github.com/qlawton/QTrack">QTrack</a>, following the method described in <a href="https://doi.org/10.1175/MWR-D-21-0321.1">Lawton et al. (2022)</a>. Note that starting in 2024, tracks including both the ATL and EPAC basins will be included. AEW Track data is updated on a yearly basis, typically in January of the following year. However, because the year 2024 uses preliminary HURDAT data to determine "developing" AEW classification, users are cautioned that these designiations could change pending finialization of the 2024 HURDAT year.  Updated with Eastern Pacific Tracks: New easterly tracks have been produced through 2024 that included systems over the Eastern Pacific basin in addition to those over the Atlantic Ocean. These are stored as "ERA5_WITH_EPAC" and "MERRA2_WITH_EPAC". Please note that while the Atlantic Ocean tracks have been evaluated, the Eastern Pacific tracks have not. So use these data cautiously.  If you use this data for your research, please cite: <blockquote> Lawton, Q. A., S. J. Majumdar, K. Dotterer, C. Thorncroft, and C. J. Schreck, 2022: The Influence of Convectively Coupled Kelvin Waves on African Easterly Waves in a Wave-Following Framework. Monthly Weather Review, 150(8), 2055-2072, <a href="https://doi.org/10.1175/MWR-D-21-0321.1" rel="nofollow">https://doi.org/10.1175/MWR-D-21-0321.1</a>. </blockquote> GitHub Repo for QTrack can be found here: <a href="https://github.com/qlawton/QTrack">https://github.com/qlawton/QTrack</a> Installable Python Module can be found here: <a href="https://pypi.org/project/qtrack/">https://pypi.org/project/qtrack/</a>

opencc-by-4.0Oct 2024View details →

I2K 2024 - Object Tracking and Track Analysis using TrackMate and CellTracksColab

Datasets for I2K 2024 - Object Tracking and Track Analysis using TrackMate and CellTracksColab -workshop Course walk through: I2K_2024: <a href="https://github.com/CellMigrationLab/I2K_2024" target="_blank" rel="noopener">https://github.com/CellMigrationLab/I2K_2024</a> V4SBD_Winter_school_2025: <a href="https://github.com/CellMigrationLab/V4SDB_Winter_School_2025" target="_blank" rel="noopener">https://github.com/CellMigrationLab/V4SDB_Winter_School_2025</a>

opencc-by-4.0May 2023View details →

Serializing Java Objects in Plain Code

This is the anonymized replication package for "Serializing Java Objects in Plain Code". The zip file contains the following directory structure: <pre><code>. ├── BroadleafCommerce ├── BroadleafDemoSite ├── generated-tests ├── gephi ├── graphhopper ├── pdfbox └── prodj</code></pre> The tool exists under "prodj", the generated tests can be found in "generated-tests" and the other projects are in their respective directories and are configured to run with prodj. You can run prodj from the "prodj/rockstofetch" directory by executing `java -jar target/rocks-to-fetch.jar --help`. Our configurations can be found in "prodj/rockstofetch/src/test/resources".